Saliency detection for surface defects of ceramic tile

Considering the lack of techniques for automatically detecting the surface defects of ceramic tiles, owing to the numerous types and complex surface textures of ceramic tiles, a salient region detection (SRD) algorithm is proposed. After the superpixel segmentation of the surface image of ceramic tiles, colour and structure saliency maps were constructed by taking the differences between the defects and backgrounds, in terms of colour and structure, as the salient values. The final saliency map for the surface defects was then obtained through the fusion of these two maps. The salient regions in the map were determined using the connected domain method and were then marked using minimum circumscribed rectangles for detecting the surface defects. The feasibility and effectiveness of the proposed SRD algorithm was verified through comparative experiments. The experi-mental results show that the detection accuracy of the algorithm for unknown types of ceramic tiles is 92.60%, which was 5.40% higher than that of the ResNet-50 algorithm, which was the highest for known types of ceramic tiles.

Automated summary

A new algorithm for automatically detecting surface defects of ceramic tiles was proposed, which utilizes salient region detection and fusion of color and structure saliency maps to determine defect regions, showing a high detection accuracy.